Not every problem can be solved by machine learning, and not every company is poised to apply AI. Here’s how to know whether your IT organization is ready to reap the benefits of artificial intelligence.
Now this is pretty interesting. I would’ve thought that all companies were ready to use AI. But now I am finding that isn’t true. Here’s what I have found. Each of the 10 points below have pluses and minuses for consideration.
- There’s plenty of data – You have all the information that you need. The question is, will you use the right model to find the answer to your questions?
- There are enough data scientists – Are you hiring people who say they can really do it?
- The company can track/acquire factors that matter – Is all the data there or is some missing from the models?
- The company has ways to clean and change the data when needed – Do you really have everything that you need to clean the noise and change when needed?
- Statistical analysis has already been completed as exploration – Use the right analysis after finding out the information, not before. It can lead up to overdetermined systems.
- You use many different approaches to find the best model to find the data you need – Do not use what you are comfortable using.
- The company has the capacity to train deep learning models – It takes more time as the system has to learn more.
- The machine learning (ML) models outperform the statistical models – If the statistical model cannot raise the bar for the ML model problems can arise.
- The system can deploy predictive models – It has to run on the cloud, server, PC or smart phone.
- It can update models from time to time – It has to update as data always changes. If updating isn’t completed there will be errors until fixed.
Intriguing isn’t it? Just make sure that you follow the suggestions so that your company is ready for AI implementation.